Biochemical signal transduction is a critical basic cellular process by which information travels from the receptors on the surface of a cell to the nucleus where responses to the signals are generated. Originally thought to be a relatively simple system that merely passed along signals, signal transduction has proven to be a highly complicated system of molecules whose interactions have yet to be fully understood. Furthermore, a number of disease states such as cancer, diabetes, and neurological disorders have their origins in malfunctioning signal transduction components. Until signal transduction is understood in its totality, rational intervention of these diseases will be significantly hampered. While conventional laboratory methods have been used to study individual components or pathways involved in signal transduction, they have less utility in studying how signal transduction systems work as a whole and therefore do not detect higher levels or organization and regulation that are known to exist in other complex systems. In this application, it is proposed that signal transduction systems be mathematically modeled and analyzed in order to determine the role of complexity in the system. It is hypothesized that signal transduction involves both the movement and processing of information in such a way as to provide a cell the capacity for decision making, something every cell must possess at some level. The most advanced techniques in the field of chaos theory and neural networks (the basis of artificial intelligence) will be applied to determine whether biochemical signal transduction systems have the fundamental features of a complex system, how those features might be used for cell level decision making, and how mutations in the system might affect the higher level functions of the system. The elucidation of such higher levels of function in signal transduction systems would greatly enhance our understanding of this basic cellular function and make the rational design of therapy more effective.